Prognostic value of total lesion glycolysis and metabolic active tumor volume in non-small cell lung cancer

Volumetric parameters of the primary tumor and whole-body tumor burden derived from baseline 18F-FDG PET/CT can predict overall survival in non-small cell lung cancer patients: initial results from a single institution

BACKGROUND

Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) are volumetric parameters derived from ¹⁸F-FDG PET/CT, suggested to have a prognostic value in cancer patients. Our study aimed to test whether these volumetric parameters of the primary tumor and whole-body tumor burden (WBTB) can predict overall survival (OS) in non-small cell lung cancer (NSCLC) patients.

MATERIALS AND METHODS

Thirty biopsy-proven NSCLC patients who had not begun anti-tumor therapy were included in this prospective study. A baseline ¹⁸F-FDG PET/CT study was acquired. Scans were interpreted visually and semi-quantitatively by drawing a 3D volume of interest (VOI) over the primary tumor and all positive lesions to calculate metabolic, volumetric parameters, and WBTB. The PET parameters were used to stratify patients into high- and low-risk categories. The overall survival was estimated from the date of scanning until the date of death or last follow-up.

RESULTS

At a median follow-up of 22.73 months, the mean OS was shorter among patients with higher tu MTV and tu TLG and high WBTB. High WB TLG was independently associated with the risk of death (p < 0.025). Other parameters, e.g., SUV_max, SUV_peak, and SUV_mean, were not predictive of outcomes in these patients.

CONCLUSION

In patients with NSCLC, tu MTV, tu TLG, and WBTB determined on initial staging ¹⁸F-FDG PET/CT seems to be a strong, independent imaging biomarker to predict OS, superior to the clinical assessment of the primary tumor. The WB TLG was found to be the best predictor of OS.

Targeting glycolysis in non-small cell lung cancer: Promises and challenges

Metabolic disturbance, particularly of glucose metabolism, is a hallmark of tumors such as non-small cell lung cancer (NSCLC). Cancer cells tend to reprogram a majority of glucose metabolism reactions into glycolysis, even in oxygen-rich environments. Although glycolysis is not an efficient means of ATP production compared to oxidative phosphorylation, the inhibition of tumor glycolysis directly impedes cell survival and growth. This review focuses on research advances in glycolysis in NSCLC and systematically provides an overview of the key enzymes, biomarkers, non-coding RNAs, and signaling pathways that modulate the glycolysis process and, consequently, tumor growth and metastasis in NSCLC. Current medications, therapeutic approaches, and natural products that affect glycolysis in NSCLC are also summarized. We found that the identification of appropriate targets and biomarkers in glycolysis, specifically for NSCLC treatment, is still a challenge at present. However, LDHB, PDK1, MCT2, GLUT1, and PFKM might be promising targets in the treatment of NSCLC or its specific subtypes, and DPPA4, NQO1, GAPDH/MT-CO1, PGC-1α, OTUB2, ISLR, Barx2, OTUB2, and RFP180 might be prognostic predictors of NSCLC. In addition, natural products may serve as promising therapeutic approaches targeting multiple steps in glycolysis metabolism, since natural products always present multi-target properties. The development of metabolic intervention that targets glycolysis, alone or in combination with current therapy, is a potential therapeutic approach in NSCLC treatment. The aim of this review is to describe research patterns and interests concerning the metabolic treatment of NSCLC.

The value of the Standardized Uptake Value (SUV) and Metabolic Tumor Volume (MTV) in lung cancer

The diagnosis, staging and therapeutic monitoring of lung cancer were amongst the first applications for which the utility of FDG PET was documented and FDG PET/CT is now a routine diagnostic tool for clinical decision-making. As well as having high sensitivity for detection of disease sites, which provides critical information about stage, the intensity of uptake provides deeper biological characterization, while the burden of disease also has potential clinical significance. These disease characteristics can easily be quantified on delayed whole-body imaging as the maximum standardized uptake value (SUV_max) and metabolic tumor volume (MTV), respectively. There have been significant efforts to harmonize the measurement of these features, particularly within the context of clinical trials. Nevertheless, however calculated, in general, a high SUV_max and large MTV have been shown to have an adverse prognostic significance. Nevertheless, the use of these parameters in the interpretation and reporting of clinical scans remains inconsistent and somewhat controversial. This review details the current status of semi-quantitative FDG PET/CT in the evaluation of lung cancer.

Unraveling the Rewired Metabolism in Lung Cancer Using Quantitative NMR Metabolomics

Lung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients.

Plausibility and redundancy analysis to select FDG-PET textural features in non-small cell lung cancer

Background

Radiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non‐small cell lung cancer (NSCLC) dataset.

Materials and methods

The proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1‐yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.

Results

The number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4–10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).

Conclusion

When performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

PET-Based Volumetric Biomarkers for Risk Stratification of Non-Small Cell Lung Cancer Patients

Despite the recent advances in lung cancer biology, molecular pathology, and treatment, this malignancy remains the leading cause of cancer-related death worldwide and non-small cell lung cancer (NSCLC) is the most common form found at diagnosis. Accurate staging of the disease is a fundamental prognostic factor that correctly predicts progression-free (PFS) and overall survival (OS) of NSCLC patients. However, outcome of patients within each TNM staging group can change widely highlighting the need to identify additional prognostic biomarkers to better stratify patients on the basis of risk. 18F-FDG PET/CT plays an essential role in staging, evaluation of treatment response, and tumoral target delineation in NSCLC patients. Moreover, a number of studies showed the prognostic role of imaging parameters derived from PET images, such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG). These parameters represent three-dimensional PET-based measurements providing information on both tumor volume and metabolic activity and previous studies reported their ability to predict OS and PFS of NSCLC patients. This review will primarily focus on the studies that showed the prognostic and predictive role of MTV and TLG in NSCLC patients, addressing also their potential utility in the new era of immunotherapy of NSCLC.

Repeatability of two semi-automatic artificial intelligence approaches for tumor segmentation in PET

Background

Positron emission tomography (PET) is routinely used for cancer staging and treatment follow-up. Metabolic active tumor volume (MATV) as well as total MATV (TMATV—including primary tumor, lymph nodes and metastasis) and/or total lesion glycolysis derived from PET images have been identified as prognostic factor or for the evaluation of treatment efficacy in cancer patients. To this end, a segmentation approach with high precision and repeatability is important. However, the implementation of a repeatable and accurate segmentation algorithm remains an ongoing challenge.

Methods

In this study, we compare two semi-automatic artificial intelligence (AI)-based segmentation methods with conventional semi-automatic segmentation approaches in terms of repeatability. One segmentation approach is based on a textural feature (TF) segmentation approach designed for accurate and repeatable segmentation of primary tumors and metastasis. Moreover, a convolutional neural network (CNN) is trained. The algorithms are trained, validated and tested using a lung cancer PET dataset. The segmentation accuracy of both segmentation approaches is compared using the Jaccard coefficient (JC). Additionally, the approaches are externally tested on a fully independent test–retest dataset. The repeatability of the methods is compared with those of two majority vote (MV2, MV3) approaches, 41%SUV_MAX, and a SUV > 4 segmentation (SUV4). Repeatability is assessed with test–retest coefficients (TRT%) and intraclass correlation coefficient (ICC). An ICC > 0.9 was regarded as representing excellent repeatability.

Results

The accuracy of the segmentations with the reference segmentation was good (JC median TF: 0.7, CNN: 0.73). Both segmentation approaches outperformed most other conventional segmentation methods in terms of test–retest coefficient (TRT% mean: TF: 13.0%, CNN: 13.9%, MV2: 14.1%, MV3: 28.1%, 41%SUV_MAX: 28.1%, SUV4: 18.1%) and ICC (TF: 0.98, MV2: 0.97, CNN: 0.99, MV3: 0.73, SUV4: 0.81, and 41%SUV_MAX: 0.68).

Conclusion

The semi-automatic AI-based segmentation approaches used in this study provided better repeatability than conventional segmentation approaches. Moreover, both algorithms lead to accurate segmentations for both primary tumors as well as metastasis and are therefore good candidates for PET tumor segmentation.

Ligustilide inhibits the proliferation of non-small cell lung cancer via glycolytic metabolism

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related death worldwide. The abnormal activation of glycolytic metabolism and PTEN/AKT signaling in NSCLC cells are highly correlated with their proliferation abilities and viability. Ligustilide is one of the major bioactive components of multiple Chinese traditional medicine including Angelica sinensis and Ligusticum. Ligustilide exposure inhibits the proliferation and viability of multiple cancer cell lines in vitro. However, the impact of ligustilide to the progression of NSCLC and its detailed pharmacological mechanisms remain unclear. In this research, CCK-8 and colony formation assay were performed to demonstrate ligustilide treatment inhibited the viability and proliferation ability of NSCLC cells in vitro. Caspase-3/-7 activity assay and nucleosome ELISA assay were utilized to show ligustilide promoted the apoptosis of NSCLC cells. Metabolic analysis and qRT-PCR assay were used to demonstrated that ligustilide dampened aerobic glycolysis of NSCLC cells. Nude mice were exposed to 5 mg/kg ligustilide and ligustilide inhibited orthotopic NSCLC growth in vivo. qRT-PCR and Western blot analysis were performed to substantiate the regulatory function of ligustilide to PTEN/AKT signaling in NSCLC cells. Overall, this study revealed that ligustilide regulated the proliferation, apoptosis and aerobic glycolysis of NSCLC cells through PTEN/AKT signaling pathway.

Asphericity of tumor FDG uptake in non-small cell lung cancer: reproducibility and implications for harmonization in multicenter studies

Background

Asphericity (ASP) of the primary tumor’s metabolic tumor volume (MTV) in FDG-PET/CT is independently predictive for survival in patients with non-small cell lung cancer (NSCLC). However, comparability between PET systems may be limited. Therefore, reproducibility of ASP was evaluated at varying image reconstruction and acquisition times to assess feasibility of ASP assessment in multicenter studies.

Methods

This is a retrospective study of 50 patients with NSCLC (female 20; median age 69 years) undergoing pretherapeutic FDG-PET/CT (median 3.7 MBq/kg; 180 s/bed position). Reconstruction used OSEM with TOF_4/16 (iterations 4; subsets 16; in-plane filter 2.0, 6.4 or 9.5 mm), TOF_4/8 (4 it; 8 ss; filter 2.0/6.0/9.5 mm), PSF + TOF_2/17 (2 it; 17 ss; filter 2.0/7.0/10.0 mm) or Bayesian-penalized likelihood (Q.Clear; beta, 600/1750/4000). Resulting reconstructed spatial resolution (FWHM) was determined from hot sphere inserts of a NEMA IEC phantom. Data with approx. 5-mm FWHM were retrospectively smoothed to achieve 7-mm FWHM. List mode data were rebinned for acquisition times of 120/90/60 s. Threshold-based delineation of primary tumor MTV was followed by evaluation of relative ASP/SUVmax/MTV differences between datasets and resulting proportions of discordantly classified cases.

Results

Reconstructed resolution for narrow/medium/wide in-plane filter (or low/medium/high beta) was approx. 5/7/9 mm FWHM. Comparing different pairs of reconstructed resolution between TOF_4/8, PSF + TOF_2/17, Q.Clear and the reference algorithm TOF_4/16, ASP differences was lowest at FWHM of 7 versus 7 mm. Proportions of discordant cases (ASP > 19.5% vs. ≤ 19.5%) were also lowest at 7 mm (TOF_4/8, 2%; PSF + TOF_2/17, 4%; Q.Clear, 10%). Smoothing of 5-mm data to 7-mm FWHM significantly reduced discordant cases (TOF_4/8, 38% reduced to 2%; PSF + TOF_2/17, 12% to 4%; Q.Clear, 10% to 6%), resulting in proportions comparable to original 7-mm data. Shorter acquisition time only increased proportions of discordant cases at < 90 s.

Conclusions

ASP differences were mainly determined by reconstructed spatial resolution, and multicenter studies should aim at comparable FWHM (e.g., 7 mm; determined by in-plane filter width). This reduces discordant cases (high vs. low ASP) to an acceptable proportion for TOF and PSF + TOF of < 5% (Q.Clear: 10%). Data with better resolution (i.e., lower FWHM) could be retrospectively smoothed to the desired FWHM, resulting in a comparable number of discordant cases.

The association between active tumor volume, total lesion glycolysis and levels of S-100B and LDH in stage IV melanoma patients

INTRODUCTION

The Standardized Uptake Value (SUV) in single lesions on ¹⁸F-FDG PET/CT scans and serum S-100B concentrations are inversely associated with disease-free survival in stage IV melanoma. The aim of this study was to assess the association between biomarkers (S-100B, LDH) and the PET-derived metrics SUV_mean/max, metabolic active tumor volume (MATV), and total lesion glycolysis (TLG) in stage IV melanoma in order to understand what these biomarkers reflect and their possible utility for follow-up.

METHODS

In 52 stage IV patients the association between PET-derived metrics and the biomarkers S-100B and LDH was assessed and the impact on survival analyzed.

RESULTS

S-100B was elevated (>0.15 μg/l) in 37 patients (71%), LDH in 11 (21%). There was a correlation between S-100B and LDH (R² = 0.19). S-100B was correlated to both MATV (R² = 0.375) and TLG (R² = 0.352), but LDH was not. Higher MATV and TLG levels were found in patients with elevated S-100B (p < 0.001) and also in patients with elevated LDH (>250 U/l) (p < 0.001). There was no association between the biomarkers and SUV_mean/max. Survival analysis indicated that LDH was the only predictor of melanoma-specific survival.

CONCLUSION

In newly diagnosed stage IV melanoma patients S-100B correlates with ¹⁸F-FDG PET/CT derived MATV and TLG in contrast to LDH, is more often elevated than LDH (71% vs. 21%) and seems to be a better predictor of disease load and disease progression. However, elevated LDH is the only predictor for survival. The biomarkers, S-100B and LDH appear to describe different aspects of the extent of metastatic disease and of tumornecrosis.

PET-detectable tau pathology correlates with long-term neuropsychiatric outcomes in patients with traumatic brain injury

Tau deposits is a core feature of neurodegenerative disorder following traumatic brain injury (TBI). Despite ample evidence from post-mortem studies demonstrating exposure to both mild-repetitive and severe TBIs are linked to tau depositions, associations of topology of tau lesions with late-onset psychiatric symptoms due to TBI have not been explored. To address this issue, we assessed tau deposits in long-term survivors of TBI by PET with 11C-PBB3, and evaluated those associations with late-life neuropsychiatric outcomes. PET data were acquired from 27 subjects in the chronic stage following mild-repetitive or severe TBI and 15 healthy control subjects. Among the TBI patients, 14 were diagnosed as having late-onset symptoms based on the criteria of traumatic encephalopathy syndrome. For quantification of tau burden in TBI brains, we calculated 11C-PBB3 binding capacity (cm3), which is a summed voxel value of binding potentials (BP*ND) multiplied by voxel volume. Main outcomes of the present study were differences in 11C-PBB3 binding capacity between groups, and the association of regional 11C-PBB3 binding capacity with neuropsychiatric symptoms. To confirm 11C-PBB3 binding to tau deposits in TBI brains, we conducted in vitro PBB3 fluorescence and phospho-tau antibody immunofluorescence labelling of brain sections of chronic traumatic encephalopathy obtained from the Brain Bank. Our results showed that patients with TBI had higher 11C-PBB3 binding capacities in the neocortical grey and white matter segments than healthy control subjects. Furthermore, TBI patients with traumatic encephalopathy syndrome showed higher 11C-PBB3 binding capacity in the white matter segment than those without traumatic encephalopathy syndrome, and regional assessments revealed that subgroup difference was also significant in the frontal white matter. 11C-PBB3 binding capacity in the white matter segment correlated with the severity of psychosis. In vitro assays demonstrated PBB3-positive tau inclusions at the depth of neocortical sulci, confirming 11C-PBB3 binding to tau lesions. In conclusion, increased 11C-PBB3 binding capacity is associated with late-onset neuropsychiatric symptoms following TBI, and a close correlation was found between psychosis and 11C-PBB3 binding capacity in the white matter.

18F-FDG PET/CT total lesion glycolysis is associated with circulating tumor cell counts in patients with stage I to IIIA non-small cell lung cancer

Background

In non-small cell lung cancer (NSCLC), ¹⁸F-fluoro-2-deoxy-D-glucose (¹⁸F-FDG) uptake determined by PET and presence of circulating tumor cells (CTCs) in the peripheral blood independently predict outcomes. For ¹⁸F-FDG PET/CT staging interpretation, standardized uptake values (SUV_max/avg) are routinely used in clinical reporting. The goal was to investigate whether ¹⁸F-FDG uptake measured by SUV_max/avg, but also measures of metabolic tumor volume (MTV) and total lesion glycolysis (TLG) (MTV × SUV_avg), are associated with CTCs.

Methods

Prospectively, 7.5 mL blood was drawn from NSCLC patients at the time of staging 18F-FDG PET/CT and from healthy control subjects. CTCs were identified by immunofluorescent staining (CK8/18/19^pos/EpCAM^pos/CD45^neg/DAPI^pos nucleus). ¹⁸F-FDG PET/CTs were analyzed for SUV_max, SUV_avg, MTV, and TLG.

Results

In 16 NSCLC patients with stage I–IIIA, MTV and TLG, in contrast to SUV_max and SUV_avg, were positively associated with CTCs (linear regression analysis). No CTCs were detectable in 20 healthy control subjects.

Conclusions

This pilot study demonstrates that ¹⁸F-FDG PET/CT TLG correlates with CTCs in NSCLC patients without distant metastases. TLG might be a more appropriate marker for hematogenous micrometastatic potential than SUVs.

PET segmentation of bulky tumors: Strategies and workflows to improve inter-observer variability

Background

PET-based tumor delineation is an error prone and labor intensive part of image analysis. Especially for patients with advanced disease showing bulky tumor FDG load, segmentations are challenging. Reducing the amount of user-interaction in the segmentation might help to facilitate segmentation tasks especially when labeling bulky and complex tumors. Therefore, this study reports on segmentation workflows/strategies that may reduce the inter-observer variability for large tumors with complex shapes with different levels of user-interaction.

Methods

Twenty PET images of bulky tumors were delineated independently by six observers using four strategies: (I) manual, (II) interactive threshold-based, (III) interactive threshold-based segmentation with the additional presentation of the PET-gradient image and (IV) the selection of the most reasonable result out of four established semi-automatic segmentation algorithms (Select-the-best approach). The segmentations were compared using Jaccard coefficients (JC) and percentage volume differences. To obtain a reference standard, a majority vote (MV) segmentation was calculated including all segmentations of experienced observers. Performed and MV segmentations were compared regarding positive predictive value (PPV), sensitivity (SE), and percentage volume differences.

Results

The results show that with decreasing user-interaction the inter-observer variability decreases. JC values and percentage volume differences of Select-the-best and a workflow including gradient information were significantly better than the measurements of the other segmentation strategies (p-value<0.01). Interactive threshold-based and manual segmentations also result in significant lower and more variable PPV/SE values when compared with the MV segmentation.

Conclusions

FDG PET segmentations of bulky tumors using strategies with lower user-interaction showed less inter-observer variability. None of the methods led to good results in all cases, but use of either the gradient or the Select-the-best workflow did outperform the other strategies tested and may be a good candidate for fast and reliable labeling of bulky and heterogeneous tumors.

Prognostic value of [18F]FDG PET/CT parameters in surgically resected primary lung adenocarcinoma: a single-center experience

OBJECTIVE

To evaluate the prognostic role of maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) measured by FDG-positron emission tomography (PET)/computed tomography (CT) in patients with primary lung adenocarcinoma undergoing surgical resection.

METHODS

All consecutive patients undergoing curative surgery for primary lung adenocarcinoma at the Thoracic Surgery Unit of the University Hospital of Parma between January 2009 and December 2014 were retrospectively analyzed. The cutoff point of each continuous PET parameter was determined through receiver operating characteristic curve and Youden index, using overall survival (OS) as the classification status. Univariate and multivariate Cox proportional hazards models were applied to evaluate the association between OS and potential prognostic variables, including SUVmax, MTV, and TLG.

RESULTS

A total of 193 patients were considered eligible for this study. The mean 5-year OS rate was 70.5 ± 3.5%. Acinar and lepidic patterns were more frequently associated with absent or low (<2.5) SUVmax values [18F]FDG uptake. At univariate analysis, male sex, advanced stage, micropapillary and solid pattern, lymphatic, blood vessels and pleural invasion, high SUVmax, MTV, and TLG were significantly associated with poorer OS. Multivariate analyses revealed that only sex, stage, and TLG were independent factors for OS, with male sex, stage 3+4, and high TLG value (p = 0.041) significantly associated with poorer OS.

CONCLUSIONS

In this study, [18F]FDG PET/CT parameters SUVmax, MTV, and TLG were prognostic factors in patients with surgically resected lung adenocarcinoma, able to predict OS and helping to further stratify these patients into prognostic subsets. Elevated TLG was also an independent predictor for shorter OS.

PET and MRI based RT treatment planning: Handling uncertainties

Imaging provides the basis for radiotherapy. Multi-modality images are used for target delineation (primary tumor and nodes, boost volume) and organs at risk, treatment guidance, outcome prediction, and treatment assessment. Next to anatomical information, more and more functional imaging is being used. The current paper provides a brief overview of the different applications of imaging techniques used in the radiotherapy process, focusing on uncertainties and QA. The paper mainly focuses on PET and MRI, but also provides a short discussion on DCE-CT. A close collaboration between radiology, nuclear medicine and radiotherapy departments provides the key to improve the quality of radiotherapy. Jointly developed imaging protocols (RT position setup, immobilization tools, lasers, flat table…), and QA programs are mandatory. For PET, suitable windowing in consultation with a Nuclear Medicine Physician is crucial (differentiation benign/malignant lesions, artifacts…). A basic knowledge of MRI sequences is required, in such a way that geometrical distortions are easily recognized by all members the RT and RT physics team. If this is not the case, then the radiologist should be introduced systematically in the delineation process and multidisciplinary meetings need to be organized regularly. For each image modality and each image registration process, the associated uncertainties need to be determined and integrated in the PTV margin. When using functional information for dose painting, response assessment or outcome prediction, collaboration between the different departments is even more important. Limitations of imaging based biomarkers (specificity, sensitivity) should be known.